Written languages have generally been optimized for the most meaningful elements of speech, so sometimes strange workarounds are required to capture certain subtleties: e.g. capital letters for YELLING, alternating capitalization for “thE spEaKer is VEry StUpid,” ellipses for a stilted-last-gasps sort of speech (“tell… them… the… killer… was…”), or the HTML-inspired “/s” for “please interpret the former sentence sarcastically.”

The issue:

Unfortunately, although the workarounds above are generally sufficient for human languages, they fail for non-human sounds: for example, a dog barking or a bird chirping.

If we want to write down bird songs or distinguish between different dog barks, our vocabulary is limited to various stereotyped sounds (e.g. “bark,” “woof,” “yip,” “growl”), as shown in Figure 1.

These words have very little relationship to the actual sounds that the animals are making, however!

Fig. 1: Latin letters do not allow the expression of more than a few types of dog barks. Outrageous!

So if we want to write down exactly a very specific dog bark, we are out of luck.

Proposal:

Out of luck until now, that is! What we need is a special dog-bark alphabet (Figure 2) that can capture both the range of dog sounds and their pitch.

Fig. 2: What we need is a new set of letters specifically for representing dog sounds.

In this case, the new alphabet works as follows:

It is a fully-featured alphabet, with each sound corresponding to one of the perhaps few-hundred basic dog vocalizations. As an upper bound, it’s probably reasonable to assume that we will need no more than 250 letters.

The vertical position of the letters will indicate higher or lower pitch, just like musical notes on a staff.

Figure 3 shows an example of a hypothesized candidate alphabet, where the dog noises from Figure 1 have been converted into a new sound-and-pitch-based alphabet.

Fig. 3: This poorly-documented alphabet nevertheless conveys the basic idea that 1) vertical position is pitch and 2) that dog noises are drawn from a fixed set of symbols (e.g. the “Ѱ”-like character being used for the start of a growl).

Conclusion:

Once this is successful, it will open up new jobs for linguists in creating bird alphabets, cat alphabets, and whale alphabets.

This expanded-alphabet idea can also be applied for humans, allowing us to represent common sounds that still have no adequate textual approximation, like the sound of a sneeze or yawn.

PROS: Strategic addition of new dog-sound-related words could legitimize a few new and useful Scrabble words (possible candidate dog sounds: “RR,” “GR,” “RF”).

CONS: Possibly would be substantially more effort to learn than just learning 10 synonyms for “bark,” which is the current status quo.

Once you know how to read, it’s impossible to see text the same way as you did before—you will inescapably recognize the symbols as letters the instant that you see them.

The issue:

This “automatic” parsing of written language makes it easy to forget how much effort was required to initially learn how to read. This inhibits people’s ability to empathize with children and second-language learners as they acquire literacy.

Proposal:

In order to let you remember what it was like to not be able to read, this hypothetical browser plug-in will simply change all web fonts to an illiteracy-simulating “dingbats” font (Figure 1).

Fig. 1: With the “Wingdings” font replacing the standard web page font, every Internet site becomes totally incomprehensible, letting you re-experience the lack of ability to read. In order to obtain proficiency with this new alphabet, a user would need to learn 26 lower-case letters, 26 upper-case letters, ~10 punctuation marks, and 10 digits, for a grand total of ~70–80 symbols.

Note that the new “letters” actually do directly correspond to the letters of the English alphabet, so you could hypothetically re-experience the alphabet-learning experience by using this plugin.

Fig. 2: Here is what a block of English text might look like to someone who is totally unfamiliar with Latin letters.

Fig. 3: The importance of heraldry and easily-understood symbols is more evident when you cannot read!

Fig. 4: This approximates what a medieval peasant would have experienced reading a manuscript about the Hundred Year’s War. Note how much more important the images seem when you can’t read the text.

Secret bonus feature:

If you set your browser to a “dingbats” font and actually learn how to read it, then you’ll be able to thwart spies who try to read your screen over your shoulder. The CIA should mandate that all of its laptops be set to this custom font mode.

Conclusion:

If you want to remember what it was like before you could read, you should set your browser font to Wingdings or another “dingbats” font.

PROS: Increases ability to empathize with people learning to read. Makes it difficult for spies to read your secrets.

CONS: Your browser might get stuck in this mode, and then you’d have to learn a totally new (yet almost completely useless) alphabet.

Additionally, “right” can additionally mean “correct,” which leads to the exchange:

“Should I turn left here?”

“Right.”

This is stupid and must be fixed if English is going to remain competitive with the world’s top languages, like Esperanto (Figure 1) and Loglan.

Fig. 1: Whoa, Esperanto has its own flag, it must be pretty popular!

Fig. 2: These words are bad for indicating directions. If you use them, please take a moment to feel bad about it.

Proposal:

Instead of using random words like “left” or “right,” let’s use some words that inherently have left-right properties to them.

In English, the alphabet always comes in this order

A B C D … W X Y Z

The leftmost letter (or a similar word) can be the new word for “left,” and likewise with the rightmost letter.

So “left” becomes can become “Aa,” which is actually already a Scrabble word (among other options, this one: Aa). It could be pronounced either with the a in “bat” (aa-aa) or the a in “law” (ah-ah). Or a combination, like “aa-ah.”

“Right” will then become “Zz,” which is, obviously, pronounced “zi-zuh,” as if you extended the end of the word “pizza.”

An alternative option would be to pick a multi-syllabic word that everyone knows, and use the left part of that word as “left” and the right part of that word as “right.”

Plenty of words would be suitable, but here are two proposals (Figures 4 and 5):

Fig. 4: “Alfa” or “alpha” for left and “bet” or “beta” for right might be acceptable and easy to remember.

Fig. 5: The best word is clearly “aardvark,” which splits cleanly into “aard” and “vark.” These new words have the advantage of being extremely distinct from each other and not colliding with any existing English words.

See how difficult this ORIGINAL English exchange is:

DRIVER: “Should I turn left at the next intersection?”

PASSENGER: “Right. Then once there’s no road left, right.”

Q: Which way should the car go? But see how much clearer it becomes with our new and improved words:

DRIVER: “Should I turn aard at the next intersection?”

PASSENGER: “Right. Then once there’s no road left, vark.”

PROS: Totally unambiguous directions will now be possible, saving millions of car crashes and disasters every day.

CONS: Some old-fashioned users of “left” and “right” would need to be mercilessly ridiculed until they adopted this new system.

For programmers: helps avoid errors when programming (is that variable a lower-case “L,” or is it a capital “i”).

Fig. 1: These three extremely common symbols all look identical in many fonts and styles of handwriting. Bottom: an unambiguous form of that symbol. Top: a common way of writing the symbol shown on the bottom.

Fig. 2: A more comprehensive list of letters that are potentially confusable (although they may have subtle distinctions). The “0” and “O” and “9 / g” are probably the next-worst offenders, after the 1/I/l triplet described in Figure 1. The “7 vs 1” confusion is regional; in some European countries: the “1” is more often written with a substantial diagonal stroke, which makes the 7’s cross-bar more important. In America, the 7 is rarely written with a cross-bar, since the 1 usually has only a minor (or nonexistent) diagonal stroke.

Columns of text in a book or newspaper are generally formatted in the fully justified style (Figure 1), where the text always lines up exactly on both the left and right edges.

Fig. 1: The “justify text” button (circled in red) can be found in nearly every text editor.

The issue:

Justified text works well if columns are wide and there are a lot of words to fill out each line.

But it becomes aesthetically dubious if the columns are narrow or there aren’t enough words, which result in either:

Extremely wide spaces between words if there are too few words (example: “this______column”)

or

Excessive spacing between letters if there is only one word (example: “c__o__l__u__m__n”)

In the worst-case scenario, a column of text may look like:

This____is_____a

n__a__r__r__o__w

c__o__l__u__m__n.

See figure 2 for a comparison of fully-justified text and ragged-edge (flush left) text.

Fig. 2:Part A (left) shows a few problems with fully-justified text: “the age of” has excessive spacing and the between-letter spacing in “w i s d o m” is aesthetically questionable. Unfortunately, the ragged edge of the text in part B (formatted as “flush left / ragged right”) is not a huge improvement either.

Previously, a publisher would at least know how wide a column of text would be, so they could manually adjust the text to fit in an aesthetically-appealing fashion.

But with modern web pages and e-books, font sizes and column widths can be changed by the user—so there’s no way for a publisher to plan around it.

Proposal:

This problem can be fixed by using semantically-aware SMART justification to make each line of text an optimal length.

This is accomplished as follows:

If a line of text is too short, it can be lengthened by the following steps:

Figure 3 shows the performance of each method of text justification. The “meaning-aware SMART justification” is the only method that avoids ragged edges while also keeping a fixed amount of whitespace between words.

Fig. 3: Left: a traditional example of fully-justified text. Middle: flush-left text, with an unappealing ragged right edge. Right: the vastly improved “smart” justification method, which has been recently made possible by advances in computational technology and machine learning.

Application of this method to famous books:

Original: “But man is not made for defeat,” he said. “A man can be destroyed but not defeated.”(The Old Man and the Sea, Hemingway)

Modified with superfluous filler words and synonyms: “But man is,generally,not made for defeat,” he stated. “Basically, a man can be destroyed but, as you know, not forced to surrender.”

Modified: “War is peace. Additionally, the state offreedom is slavery. Finally, in conclusion, ignorance is strength, it must be admitted.”

Original: “In general, people only ask for advice that they may not follow it; or, if they should follow it, that they may have somebody to blame for having given it”.” (The Three Musketeers, Dumas)

Modified: “In general, people only make a request for suggestions, that those same people may not abide by it. Or, if they should in fact follow it, that those people may have somebody to blame or hold responsible for having given it”.”

PROS: This is the ONLY text-formatting method that both 1) preserves inter-word spacing AND 2) aligns text in neat columns.

Systems may not allow certain letters for certain situations; for example, if your username is “Linear B ‘stone wheel’ + Mayan jaguar glyph,” it is extremely unlikely that you will have an easy time logging into your user account.

The current failure mode is usually to display a blank rectangle instead, which is unhelpful.

Proposal:

Instead, we can use a sophisticated image-recognition system to map each letter from every language onto one or more Latin characters (Fig. 1).

Usually, this is called transliteration (https://en.wikipedia.org/wiki/Transliteration). But in this case, rather than using the sound of a symbol to convert it, we are using the symbol’s visual appearance, so it’s more like “visual-literation.”

Fig. 1: With a limited character set, it may be easy to display the “Å” as“A”, or “ñ” as “n.” But it’s unclear what should be done with the Chinese character at the bottom, which isn’t similar to any specific Latin letter.

Fig. 2:

Top: Image analysis reveals that the Chinese character (meaning “is”) can be most closely matched to the Latin capital “I.” Bottom: The Greek capital “∏” (pi) is disassembled into two Ts.

Some letters actually do somewhat resemble their Latin-ized versions (like “∏” as “TT”). However, some mappings are slightly less immediately obvious (Fig 3).

Fig. 3: Many complex symbols can—with a great degree of squinting—be matched to multi-letter strings.

Conclusion:

Linguists will love this idea, which forever solves the problem of representing multiple character sets using only the very limited Latin letters.

PROS: Gives every word in every language an unambiguous mapping to a set of (26*2) = 52 Latin letters.

CONS: Many symbols may map to the same end result (for example, “I” could be the English word “I,” or it could have been a “visual-literated” version of “是“).

Fig. 4: A collection of potential mappings from various symbols to an ASCII equivalent. Finally, the days of complex transliteration are over!

English has a large number of words with multiple syllables. We could save so much time if all these words were replaced with uniquesingle–syllable equivalents!

Proposal:

For example, in the section above, we would change the following words:

English -> Eng

number -> noim

multiple -> mult

syllable(s) –> syllb(s)

replace(d)-> roup(ed)

unique -> neek

single -> soing

equivalents(s) -> eevt(s)

The final result would be:

Eng has a large noim of words with mut syllbs. We could save so much time if all these words were rouped with neek soing-syllb eevts!

See Figure 1 for an illustration of how this would save time. This new language could be referred to as “Eng” or perhaps “one-glish” (or “1NGLISH”), as shown in Figure 2.

Figure 1: The phrase “English words with multiple syllables” in normal English in blue (top) and 1NGLISH (or just “Eng”) in yellow (bottom). Note that the 1NGLISH version is approximately 25% faster to say in this totally fabricated figure.

Figure 2: Above: a couple of possible logos that resemble ones from a bankrupt Internet company. Effective advertisement and branding is important!

Obstacle #1: Is it feasible for large quantities of people to learn a new language?

Attempts at language reform / constructed languages have failed in the past.

There are also some more-suspect configurations that occasionally work, such as:

cVccc (“balks,”)

ccVcccc (“glimpsed“)

And things that theoretically could make words, but don’t seem to actually have examples:

cccVcccc (“spranksts” <– not a word, but it has a valid pronunciation)

For the sake of argument, we’ll restrict ourselves to the “commonly supported” list above.

If we make the conservative assumption that there are only 15 “valid” vowels / consonants at each position (instead of the full list of 23), we end up with the following number of possibilities for each vowel/consonant configuration:

V, 15

Vc, 225

cV, 225

Vcc, 3,375

cVc, 3,375

ccV, 3,375

Vccc, 50,625

cVcc, 50,625

ccVc, 50,625

cccV, 50,625

ccVcc, 759,375

Adding these up, we get a total of 972,465 single-syllable utterances that would be recognized as a potentially valid English word.

Since the Oxford English Dictionary only contains < 200,000 words that are in current use (plus another ~50,000 obsolete words), there is more than enough space for every even remotely plausibly useful English word to be replaced by a totally unique single-syllable equivalent.

This will save a TON of time in communication!

Testing: Real-world speed of English vs 1NGLISH:

The testing process is as follows:

A phrase is chosen

The phrase is said TWICE, with a 0.4 second pause between repetitions

The total time of both phrases AND the pause is measured

Example: if a phrase takes exactly 1.0 seconds to say once, then it would have a score of 2.4 seconds here (2.4 = 1.0 + 1.0 + 0.4)

Below are four totally normal sentences, before and after the 1NGLISH-ification process, along with their waveforms.